Overview

Dataset statistics

Number of variables21
Number of observations2000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory328.2 KiB
Average record size in memory168.1 B

Variable types

Numeric14
Categorical7

Alerts

fc is highly correlated with pcHigh correlation
four_g is highly correlated with three_gHigh correlation
pc is highly correlated with fcHigh correlation
ram is highly correlated with price_rangeHigh correlation
three_g is highly correlated with four_gHigh correlation
price_range is highly correlated with ramHigh correlation
fc is highly correlated with pcHigh correlation
four_g is highly correlated with three_gHigh correlation
pc is highly correlated with fcHigh correlation
px_height is highly correlated with px_widthHigh correlation
px_width is highly correlated with px_heightHigh correlation
ram is highly correlated with price_rangeHigh correlation
sc_h is highly correlated with sc_wHigh correlation
sc_w is highly correlated with sc_hHigh correlation
three_g is highly correlated with four_gHigh correlation
price_range is highly correlated with ramHigh correlation
fc is highly correlated with pcHigh correlation
four_g is highly correlated with three_gHigh correlation
pc is highly correlated with fcHigh correlation
ram is highly correlated with price_rangeHigh correlation
three_g is highly correlated with four_gHigh correlation
price_range is highly correlated with ramHigh correlation
three_g is highly correlated with four_gHigh correlation
four_g is highly correlated with three_gHigh correlation
fc is highly correlated with pcHigh correlation
four_g is highly correlated with three_gHigh correlation
pc is highly correlated with fcHigh correlation
px_height is highly correlated with px_widthHigh correlation
px_width is highly correlated with px_heightHigh correlation
ram is highly correlated with price_rangeHigh correlation
sc_h is highly correlated with sc_wHigh correlation
sc_w is highly correlated with sc_hHigh correlation
three_g is highly correlated with four_gHigh correlation
price_range is highly correlated with ramHigh correlation
price_range is uniformly distributed Uniform
fc has 474 (23.7%) zeros Zeros
pc has 101 (5.1%) zeros Zeros
sc_w has 180 (9.0%) zeros Zeros

Reproduction

Analysis started2022-06-09 13:19:08.255034
Analysis finished2022-06-09 13:19:36.563911
Duration28.31 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

battery_power
Real number (ℝ≥0)

Distinct1094
Distinct (%)54.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1238.5185
Minimum501
Maximum1998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2022-06-09T21:19:36.662216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum501
5-th percentile570.95
Q1851.75
median1226
Q31615.25
95-th percentile1930.15
Maximum1998
Range1497
Interquartile range (IQR)763.5

Descriptive statistics

Standard deviation439.4182061
Coefficient of variation (CV)0.3547934133
Kurtosis-1.224143883
Mean1238.5185
Median Absolute Deviation (MAD)382
Skewness0.03189847179
Sum2477037
Variance193088.3598
MonotonicityNot monotonic
2022-06-09T21:19:36.777098image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15896
 
0.3%
6186
 
0.3%
18726
 
0.3%
13795
 
0.2%
13105
 
0.2%
10635
 
0.2%
8325
 
0.2%
14145
 
0.2%
14135
 
0.2%
18075
 
0.2%
Other values (1084)1947
97.4%
ValueCountFrequency (%)
5012
 
0.1%
5022
 
0.1%
5033
0.1%
5045
0.2%
5061
 
0.1%
5072
 
0.1%
5083
0.1%
5091
 
0.1%
5103
0.1%
5114
0.2%
ValueCountFrequency (%)
19981
 
0.1%
19971
 
0.1%
19962
0.1%
19952
0.1%
19943
0.1%
19931
 
0.1%
19922
0.1%
19914
0.2%
19892
0.1%
19881
 
0.1%

blue
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
0
1010 
1
990 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
01010
50.5%
1990
49.5%

Length

2022-06-09T21:19:36.896584image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-09T21:19:37.006196image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
01010
50.5%
1990
49.5%

Most occurring characters

ValueCountFrequency (%)
01010
50.5%
1990
49.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01010
50.5%
1990
49.5%

Most occurring scripts

ValueCountFrequency (%)
Common2000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01010
50.5%
1990
49.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01010
50.5%
1990
49.5%

clock_speed
Real number (ℝ≥0)

Distinct26
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.52225
Minimum0.5
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2022-06-09T21:19:37.091963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.5
Q10.7
median1.5
Q32.2
95-th percentile2.8
Maximum3
Range2.5
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation0.8160042089
Coefficient of variation (CV)0.5360513772
Kurtosis-1.323417222
Mean1.52225
Median Absolute Deviation (MAD)0.8
Skewness0.1780841203
Sum3044.5
Variance0.6658628689
MonotonicityNot monotonic
2022-06-09T21:19:37.198591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.5413
20.6%
2.885
 
4.2%
2.378
 
3.9%
1.676
 
3.8%
2.176
 
3.8%
2.574
 
3.7%
0.674
 
3.7%
1.470
 
3.5%
1.368
 
3.4%
267
 
3.4%
Other values (16)919
46.0%
ValueCountFrequency (%)
0.5413
20.6%
0.674
 
3.7%
0.764
 
3.2%
0.858
 
2.9%
0.958
 
2.9%
161
 
3.0%
1.151
 
2.5%
1.256
 
2.8%
1.368
 
3.4%
1.470
 
3.5%
ValueCountFrequency (%)
328
 
1.4%
2.962
3.1%
2.885
4.2%
2.755
2.8%
2.655
2.8%
2.574
3.7%
2.458
2.9%
2.378
3.9%
2.259
2.9%
2.176
3.8%

dual_sim
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
1
1019 
0
981 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
11019
50.9%
0981
49.0%

Length

2022-06-09T21:19:37.309059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-09T21:19:37.408793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
11019
50.9%
0981
49.0%

Most occurring characters

ValueCountFrequency (%)
11019
50.9%
0981
49.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
11019
50.9%
0981
49.0%

Most occurring scripts

ValueCountFrequency (%)
Common2000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
11019
50.9%
0981
49.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11019
50.9%
0981
49.0%

fc
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct20
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3095
Minimum0
Maximum19
Zeros474
Zeros (%)23.7%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2022-06-09T21:19:37.490914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q37
95-th percentile13
Maximum19
Range19
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.341443748
Coefficient of variation (CV)1.007412402
Kurtosis0.2770763246
Mean4.3095
Median Absolute Deviation (MAD)3
Skewness1.019811411
Sum8619
Variance18.84813382
MonotonicityNot monotonic
2022-06-09T21:19:37.576881image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0474
23.7%
1245
12.2%
2189
 
9.4%
3170
 
8.5%
5139
 
7.0%
4133
 
6.7%
6112
 
5.6%
7100
 
5.0%
978
 
3.9%
877
 
3.9%
Other values (10)283
14.1%
ValueCountFrequency (%)
0474
23.7%
1245
12.2%
2189
 
9.4%
3170
 
8.5%
4133
 
6.7%
5139
 
7.0%
6112
 
5.6%
7100
 
5.0%
877
 
3.9%
978
 
3.9%
ValueCountFrequency (%)
191
 
0.1%
1811
 
0.5%
176
 
0.3%
1624
 
1.2%
1523
 
1.1%
1420
 
1.0%
1340
2.0%
1245
2.2%
1151
2.5%
1062
3.1%

four_g
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
1
1043 
0
957 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
11043
52.1%
0957
47.9%

Length

2022-06-09T21:19:37.681854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-09T21:19:37.781585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
11043
52.1%
0957
47.9%

Most occurring characters

ValueCountFrequency (%)
11043
52.1%
0957
47.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
11043
52.1%
0957
47.9%

Most occurring scripts

ValueCountFrequency (%)
Common2000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
11043
52.1%
0957
47.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11043
52.1%
0957
47.9%

int_memory
Real number (ℝ≥0)

Distinct63
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.0465
Minimum2
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2022-06-09T21:19:37.877299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q116
median32
Q348
95-th percentile61
Maximum64
Range62
Interquartile range (IQR)32

Descriptive statistics

Standard deviation18.14571496
Coefficient of variation (CV)0.5662307882
Kurtosis-1.21607403
Mean32.0465
Median Absolute Deviation (MAD)16
Skewness0.05788932785
Sum64093
Variance329.2669712
MonotonicityNot monotonic
2022-06-09T21:19:38.156624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2747
 
2.4%
1445
 
2.2%
1645
 
2.2%
242
 
2.1%
5742
 
2.1%
740
 
2.0%
4240
 
2.0%
4439
 
1.9%
3039
 
1.9%
637
 
1.8%
Other values (53)1584
79.2%
ValueCountFrequency (%)
242
2.1%
325
1.2%
420
1.0%
536
1.8%
637
1.8%
740
2.0%
837
1.8%
935
1.8%
1036
1.8%
1134
1.7%
ValueCountFrequency (%)
6431
1.6%
6330
1.5%
6221
1.1%
6127
1.4%
6027
1.4%
5918
0.9%
5836
1.8%
5742
2.1%
5627
1.4%
5529
1.5%

m_dep
Real number (ℝ≥0)

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50175
Minimum0.1
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2022-06-09T21:19:38.268455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.2
median0.5
Q30.8
95-th percentile1
Maximum1
Range0.9
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.2884155496
Coefficient of variation (CV)0.5748192319
Kurtosis-1.274348884
Mean0.50175
Median Absolute Deviation (MAD)0.3
Skewness0.08908200979
Sum1003.5
Variance0.08318352926
MonotonicityNot monotonic
2022-06-09T21:19:38.346214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.1320
16.0%
0.2213
10.7%
0.8208
10.4%
0.5205
10.2%
0.7200
10.0%
0.3199
10.0%
0.9195
9.8%
0.6186
9.3%
0.4168
8.4%
1106
 
5.3%
ValueCountFrequency (%)
0.1320
16.0%
0.2213
10.7%
0.3199
10.0%
0.4168
8.4%
0.5205
10.2%
0.6186
9.3%
0.7200
10.0%
0.8208
10.4%
0.9195
9.8%
1106
 
5.3%
ValueCountFrequency (%)
1106
 
5.3%
0.9195
9.8%
0.8208
10.4%
0.7200
10.0%
0.6186
9.3%
0.5205
10.2%
0.4168
8.4%
0.3199
10.0%
0.2213
10.7%
0.1320
16.0%

mobile_wt
Real number (ℝ≥0)

Distinct121
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean140.249
Minimum80
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2022-06-09T21:19:38.444602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile86
Q1109
median141
Q3170
95-th percentile196
Maximum200
Range120
Interquartile range (IQR)61

Descriptive statistics

Standard deviation35.3996549
Coefficient of variation (CV)0.2524057562
Kurtosis-1.210376474
Mean140.249
Median Absolute Deviation (MAD)31
Skewness0.006558157429
Sum280498
Variance1253.135567
MonotonicityNot monotonic
2022-06-09T21:19:38.571834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18228
 
1.4%
18527
 
1.4%
10127
 
1.4%
14626
 
1.3%
19926
 
1.3%
8825
 
1.2%
10525
 
1.2%
19825
 
1.2%
8924
 
1.2%
14523
 
1.1%
Other values (111)1744
87.2%
ValueCountFrequency (%)
8021
1.1%
8113
0.7%
8215
0.8%
8319
0.9%
8417
0.9%
8513
0.7%
8619
0.9%
8715
0.8%
8825
1.2%
8924
1.2%
ValueCountFrequency (%)
20019
0.9%
19926
1.3%
19825
1.2%
19719
0.9%
19620
1.0%
19511
0.5%
19416
0.8%
19315
0.8%
19215
0.8%
19115
0.8%

n_cores
Real number (ℝ≥0)

Distinct8
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5205
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2022-06-09T21:19:38.675713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q37
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.287836718
Coefficient of variation (CV)0.5061025811
Kurtosis-1.229749767
Mean4.5205
Median Absolute Deviation (MAD)2
Skewness0.003627508314
Sum9041
Variance5.234196848
MonotonicityNot monotonic
2022-06-09T21:19:38.751225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4274
13.7%
7259
13.0%
8256
12.8%
2247
12.3%
5246
12.3%
3246
12.3%
1242
12.1%
6230
11.5%
ValueCountFrequency (%)
1242
12.1%
2247
12.3%
3246
12.3%
4274
13.7%
5246
12.3%
6230
11.5%
7259
13.0%
8256
12.8%
ValueCountFrequency (%)
8256
12.8%
7259
13.0%
6230
11.5%
5246
12.3%
4274
13.7%
3246
12.3%
2247
12.3%
1242
12.1%

pc
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct21
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9165
Minimum0
Maximum20
Zeros101
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2022-06-09T21:19:38.845356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median10
Q315
95-th percentile20
Maximum20
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.064314941
Coefficient of variation (CV)0.6115378351
Kurtosis-1.171498795
Mean9.9165
Median Absolute Deviation (MAD)5
Skewness0.01730615047
Sum19833
Variance36.77591571
MonotonicityNot monotonic
2022-06-09T21:19:38.939254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
10122
 
6.1%
7119
 
5.9%
9112
 
5.6%
20110
 
5.5%
14104
 
5.2%
1104
 
5.2%
0101
 
5.1%
299
 
5.0%
1799
 
5.0%
695
 
4.8%
Other values (11)935
46.8%
ValueCountFrequency (%)
0101
5.1%
1104
5.2%
299
5.0%
393
4.7%
495
4.8%
559
2.9%
695
4.8%
7119
5.9%
889
4.5%
9112
5.6%
ValueCountFrequency (%)
20110
5.5%
1983
4.2%
1882
4.1%
1799
5.0%
1688
4.4%
1592
4.6%
14104
5.2%
1385
4.2%
1290
4.5%
1179
4.0%

px_height
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1137
Distinct (%)56.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean645.108
Minimum0
Maximum1960
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2022-06-09T21:19:39.059768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile70.95
Q1282.75
median564
Q3947.25
95-th percentile1485.05
Maximum1960
Range1960
Interquartile range (IQR)664.5

Descriptive statistics

Standard deviation443.7808108
Coefficient of variation (CV)0.6879170787
Kurtosis-0.3158654936
Mean645.108
Median Absolute Deviation (MAD)318
Skewness0.6662712561
Sum1290216
Variance196941.408
MonotonicityNot monotonic
2022-06-09T21:19:39.173043image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3477
 
0.4%
1796
 
0.3%
3716
 
0.3%
2756
 
0.3%
5265
 
0.2%
3275
 
0.2%
6745
 
0.2%
6675
 
0.2%
3565
 
0.2%
565
 
0.2%
Other values (1127)1945
97.2%
ValueCountFrequency (%)
02
0.1%
11
 
0.1%
21
 
0.1%
32
0.1%
43
0.1%
51
 
0.1%
61
 
0.1%
71
 
0.1%
82
0.1%
91
 
0.1%
ValueCountFrequency (%)
19601
0.1%
19491
0.1%
19201
0.1%
19141
0.1%
19011
0.1%
18991
0.1%
18951
0.1%
18781
0.1%
18741
0.1%
18691
0.1%

px_width
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1109
Distinct (%)55.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1251.5155
Minimum500
Maximum1998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2022-06-09T21:19:39.296788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile579.85
Q1874.75
median1247
Q31633
95-th percentile1929.05
Maximum1998
Range1498
Interquartile range (IQR)758.25

Descriptive statistics

Standard deviation432.1994469
Coefficient of variation (CV)0.3453408663
Kurtosis-1.186005229
Mean1251.5155
Median Absolute Deviation (MAD)376
Skewness0.01478747377
Sum2503031
Variance186796.3619
MonotonicityNot monotonic
2022-06-09T21:19:39.407998image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8747
 
0.4%
12477
 
0.4%
13836
 
0.3%
14696
 
0.3%
14636
 
0.3%
14295
 
0.2%
17265
 
0.2%
19235
 
0.2%
12345
 
0.2%
12635
 
0.2%
Other values (1099)1943
97.2%
ValueCountFrequency (%)
5002
0.1%
5012
0.1%
5031
 
0.1%
5061
 
0.1%
5074
0.2%
5081
 
0.1%
5092
0.1%
5103
0.1%
5112
0.1%
5122
0.1%
ValueCountFrequency (%)
19981
 
0.1%
19971
 
0.1%
19961
 
0.1%
19953
0.1%
19942
 
0.1%
19921
 
0.1%
19911
 
0.1%
19901
 
0.1%
19893
0.1%
19885
0.2%

ram
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1562
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2124.213
Minimum256
Maximum3998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2022-06-09T21:19:39.527780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum256
5-th percentile445
Q11207.5
median2146.5
Q33064.5
95-th percentile3826.35
Maximum3998
Range3742
Interquartile range (IQR)1857

Descriptive statistics

Standard deviation1084.732044
Coefficient of variation (CV)0.5106512594
Kurtosis-1.19191307
Mean2124.213
Median Absolute Deviation (MAD)932.5
Skewness0.006628035399
Sum4248426
Variance1176643.606
MonotonicityNot monotonic
2022-06-09T21:19:39.644696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26104
 
0.2%
22274
 
0.2%
31424
 
0.2%
14644
 
0.2%
12294
 
0.2%
3153
 
0.1%
19583
 
0.1%
12773
 
0.1%
17243
 
0.1%
37033
 
0.1%
Other values (1552)1965
98.2%
ValueCountFrequency (%)
2561
0.1%
2582
0.1%
2591
0.1%
2621
0.1%
2631
0.1%
2651
0.1%
2671
0.1%
2731
0.1%
2771
0.1%
2782
0.1%
ValueCountFrequency (%)
39981
0.1%
39961
0.1%
39931
0.1%
39912
0.1%
39901
0.1%
39841
0.1%
39781
0.1%
39711
0.1%
39702
0.1%
39691
0.1%

sc_h
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct15
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.3065
Minimum5
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2022-06-09T21:19:39.753507image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6
Q19
median12
Q316
95-th percentile19
Maximum19
Range14
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.213245004
Coefficient of variation (CV)0.3423593227
Kurtosis-1.190791247
Mean12.3065
Median Absolute Deviation (MAD)4
Skewness-0.09888424098
Sum24613
Variance17.75143347
MonotonicityNot monotonic
2022-06-09T21:19:39.838281image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
17193
 
9.7%
12157
 
7.8%
7151
 
7.5%
16143
 
7.1%
14143
 
7.1%
15135
 
6.8%
13131
 
6.6%
11126
 
6.3%
10125
 
6.2%
19124
 
6.2%
Other values (5)572
28.6%
ValueCountFrequency (%)
597
4.9%
6114
5.7%
7151
7.5%
8117
5.9%
9124
6.2%
10125
6.2%
11126
6.3%
12157
7.8%
13131
6.6%
14143
7.1%
ValueCountFrequency (%)
19124
6.2%
18120
6.0%
17193
9.7%
16143
7.1%
15135
6.8%
14143
7.1%
13131
6.6%
12157
7.8%
11126
6.3%
10125
6.2%

sc_w
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct19
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.767
Minimum0
Maximum18
Zeros180
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2022-06-09T21:19:39.936494image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q39
95-th percentile14
Maximum18
Range18
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.356397606
Coefficient of variation (CV)0.7554010067
Kurtosis-0.3895227894
Mean5.767
Median Absolute Deviation (MAD)3
Skewness0.6337870734
Sum11534
Variance18.9782001
MonotonicityNot monotonic
2022-06-09T21:19:40.024259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1210
10.5%
3199
10.0%
4182
9.1%
0180
9.0%
5161
 
8.1%
2156
 
7.8%
7132
 
6.6%
6130
 
6.5%
8125
 
6.2%
10107
 
5.3%
Other values (9)418
20.9%
ValueCountFrequency (%)
0180
9.0%
1210
10.5%
2156
7.8%
3199
10.0%
4182
9.1%
5161
8.1%
6130
6.5%
7132
6.6%
8125
6.2%
997
4.9%
ValueCountFrequency (%)
188
 
0.4%
1719
 
0.9%
1629
 
1.5%
1531
 
1.6%
1433
 
1.7%
1349
2.5%
1268
3.4%
1184
4.2%
10107
5.3%
997
4.9%

talk_time
Real number (ℝ≥0)

Distinct19
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.011
Minimum2
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2022-06-09T21:19:40.126985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q16
median11
Q316
95-th percentile20
Maximum20
Range18
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.463955198
Coefficient of variation (CV)0.4962269728
Kurtosis-1.218590963
Mean11.011
Median Absolute Deviation (MAD)5
Skewness0.009511762222
Sum22022
Variance29.8548064
MonotonicityNot monotonic
2022-06-09T21:19:40.214051image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
7124
 
6.2%
4123
 
6.2%
16116
 
5.8%
15115
 
5.8%
19113
 
5.7%
6111
 
5.5%
10105
 
5.2%
8104
 
5.2%
11103
 
5.1%
20102
 
5.1%
Other values (9)884
44.2%
ValueCountFrequency (%)
299
5.0%
394
4.7%
4123
6.2%
593
4.7%
6111
5.5%
7124
6.2%
8104
5.2%
9100
5.0%
10105
5.2%
11103
5.1%
ValueCountFrequency (%)
20102
5.1%
19113
5.7%
18100
5.0%
1798
4.9%
16116
5.8%
15115
5.8%
14101
5.1%
13100
5.0%
1299
5.0%
11103
5.1%

three_g
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
1
1523 
0
477 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
11523
76.1%
0477
 
23.8%

Length

2022-06-09T21:19:40.325014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-09T21:19:40.428044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
11523
76.1%
0477
 
23.8%

Most occurring characters

ValueCountFrequency (%)
11523
76.1%
0477
 
23.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
11523
76.1%
0477
 
23.8%

Most occurring scripts

ValueCountFrequency (%)
Common2000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
11523
76.1%
0477
 
23.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11523
76.1%
0477
 
23.8%

touch_screen
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
1
1006 
0
994 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
11006
50.3%
0994
49.7%

Length

2022-06-09T21:19:40.519598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-09T21:19:40.624824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
11006
50.3%
0994
49.7%

Most occurring characters

ValueCountFrequency (%)
11006
50.3%
0994
49.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
11006
50.3%
0994
49.7%

Most occurring scripts

ValueCountFrequency (%)
Common2000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
11006
50.3%
0994
49.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11006
50.3%
0994
49.7%

wifi
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
1
1014 
0
986 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
11014
50.7%
0986
49.3%

Length

2022-06-09T21:19:40.716579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-09T21:19:40.815694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
11014
50.7%
0986
49.3%

Most occurring characters

ValueCountFrequency (%)
11014
50.7%
0986
49.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
11014
50.7%
0986
49.3%

Most occurring scripts

ValueCountFrequency (%)
Common2000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
11014
50.7%
0986
49.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11014
50.7%
0986
49.3%

price_range
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
0
500 
3
500 
2
500 
1
500 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
0500
25.0%
3500
25.0%
2500
25.0%
1500
25.0%

Length

2022-06-09T21:19:40.908669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-09T21:19:41.018677image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1500
25.0%
2500
25.0%
3500
25.0%
0500
25.0%

Most occurring characters

ValueCountFrequency (%)
1500
25.0%
2500
25.0%
3500
25.0%
0500
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1500
25.0%
2500
25.0%
3500
25.0%
0500
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common2000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1500
25.0%
2500
25.0%
3500
25.0%
0500
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1500
25.0%
2500
25.0%
3500
25.0%
0500
25.0%

Interactions

2022-06-09T21:19:33.937250image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:11.295198image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:13.159344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:14.838603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:16.633466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:18.347592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:19.998710image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:21.659900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:23.347184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:25.218838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:26.977827image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:28.693426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:30.415474image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:32.193137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:34.064156image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:11.563391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:13.277534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:15.036663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:16.754649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:18.465062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:20.118559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:21.781842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:23.486068image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:25.347733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:27.102807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:28.818291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:30.539868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:32.317801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:34.180158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:11.676810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:13.385436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:15.152957image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:16.864852image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:18.571777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:20.231929image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:21.898034image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:23.604964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:25.467682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:27.218872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:28.934475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:30.785134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:32.435119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:34.302104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:11.813282image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:13.498547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:15.275905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:16.984544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:18.683477image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:20.353574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:22.018218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:23.730685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:25.595583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:27.343850image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:29.056688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:30.901951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:32.559075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:34.422817image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:11.931947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:13.609446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:15.393884image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:17.107722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:18.791742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:20.467270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:22.136140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:23.853358image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:25.715908image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:27.464610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:29.173639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:31.016014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:32.684739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:34.536047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:12.043670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:13.712669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:15.501563image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:17.220180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:18.891709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:20.574279image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:22.245847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:23.965423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:25.830163image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2022-06-09T21:19:29.283642image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:31.123186image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:32.794446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:34.656529image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:12.165047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:13.824880image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:15.619835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:17.347132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:19.003584image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:20.687316image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:22.365091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:24.088323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:25.954103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:27.692076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:29.401366image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:31.235243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:32.915124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:34.781402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:12.290711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:13.938571image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:15.743231image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:17.468561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:19.118013image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:20.808257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:22.488751image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:24.214057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:26.079636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:27.812167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:29.523251image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:31.353365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:33.038867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:34.908772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:12.418452image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:14.064901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:15.872611image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:17.592902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:19.328749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:20.933562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:22.612670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:24.452675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:26.212315image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:27.945818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:29.652158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:31.475944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:33.165825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:35.036923image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:12.547320image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:14.185579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:16.007996image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:17.714942image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:19.443848image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:21.065819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:22.741549image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:24.581978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:26.341224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:28.073218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:29.786414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:31.596875image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:33.306804image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:35.160593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:12.672071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:14.305217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:16.134795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:17.843716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:19.557422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:21.187155image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:22.865303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:24.709144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:26.471247image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:28.200890image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:29.911293image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:31.719472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:33.445747image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:35.283105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:12.793745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:14.494954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:16.262523image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:17.968100image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:19.669983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:21.304052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:22.987051image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:24.835906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:26.597130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:28.323596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:30.034763image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:31.837649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:33.567955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:35.403000image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:12.911909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:14.605658image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:16.383875image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:18.097564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:19.776151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:21.418496image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:23.102728image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:24.956932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:26.722795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:28.447520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:30.154720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:31.950234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:33.691446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:35.526768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:13.035610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:14.722346image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:16.507802image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:18.219895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:19.885796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:21.538957image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:23.225129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:25.083890image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:26.850168image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:28.569195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:30.278621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:32.073488image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-06-09T21:19:33.812078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2022-06-09T21:19:41.134180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-06-09T21:19:41.393776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-06-09T21:19:41.658814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-06-09T21:19:41.901666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-06-09T21:19:42.052209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-06-09T21:19:35.916075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-06-09T21:19:36.373958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

battery_powerblueclock_speeddual_simfcfour_gint_memorym_depmobile_wtn_corespcpx_heightpx_widthramsc_hsc_wtalk_timethree_gtouch_screenwifiprice_range
084202.201070.61882220756254997190011
1102110.5101530.7136369051988263117371102
256310.5121410.91455612631716260311291102
361512.5000100.813169121617862769168111002
4182111.20131440.614121412081212141182151101
5185900.5130220.716417100416541067171101001
6182101.7041100.813981038110183220138181013
7195400.5100240.818740512114970016351110
8144510.5000530.71747143868361099171201000
950910.612190.193515113712245131910121000

Last rows

battery_powerblueclock_speeddual_simfcfour_gint_memorym_depmobile_wtn_corespcpx_heightpx_widthramsc_hsc_wtalk_timethree_gtouch_screenwifiprice_range
1990161712.4081360.8851974314262965371000
1991188202.00111440.811381947433579198201103
199267412.9110210.219834576180911806341110
1993146710.5000180.61225088810993962151151113
199485802.2010500.1841252814163978171631103
199579410.510120.810661412221890668134191100
1996196512.6100390.218743915196520321110161112
1997191100.9111360.710883868163230579151103
1998151200.9041460.1145553366708691810191110
199951012.0151450.9168616483754391919421113